Addressing Inter-reconstruction variability in multi-energy myocardial CT Radiomics: The Benefits of combat harmonization.

Rationale And Objectives: To investigate the effect of ComBat harmonization on the stability of myocardial radiomic features derived from multi-energy CT reconstructions.

Materials And Methods: A retrospective study was conducted on 205 patients who underwent dual-energy chest CTA at a single center. The data was reconstructed into multiple spectral reconstructions (mixed energy simulating standard 120 Kv acquisition and monoenergetic images ranging from 40 to 190 keV in increments of 10).

Posttraumatic Renal Artery-Inferior Vena Cava Fistula-Induced High-Output Cardiac Failure: A Case Study.

High-output cardiac failure is a less prevalent form of heart failure. Most patients with heart failure are typically categorized as having either systolic or diastolic dysfunction with elevated systemic vascular resistance. Individuals with high-output cardiac failure exhibit normal cardiac function and decreased systemic vascular resistance.

Motion Compensated Unsupervised Deep Learning for 5D MRI.

We propose an unsupervised deep learning algorithm for the motion-compensated reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated free-breathing 5D MRI simplifies the scan planning, improves patient comfort, and offers several clinical benefits over breath-held 2D exams, including isotropic spatial resolution and the ability to reslice the data to arbitrary views. However, the current reconstruction algorithms for 5D MRI take very long computational time, and their outcome is greatly dependent on the uniformity of the binning of the acquired data into different physiological phases.

Pulmonary Artery Stenosis After an Orthotopic Heart Transplantation: A Case Report With Cardiac Imaging Findings and a Literature Review.

Pulmonary artery stenosis is a rare complication of heart transplantation. It is typically a congenital condition or can be secondary to rheumatic fever, systemic vasculitis like Behcet's disease, or Takayasu's arteritis. It can also occur as a rarity of a delayed complication post-heart transplant.

Computed Tomography Angiography for Aortic Diseases.

Aortic pathologies encompass a heterogeneous group of disorders, including acute aortic syndrome, traumatic aortic injury , aneurysm, aortitis, and atherosclerosis. The clinical manifestations of these disorders can be varied and non-specific, ranging from acute presentations in the emergency department to chronic incidental findings in an outpatient setting. Given the non-specific nature of their clinical presentations, the reliance on non-invasive imaging for screening, definitive diagnosis, therapeutic strategy planning, and post-intervention surveillance has become paramount.

Evaluating Autoencoders for Dimensionality Reduction of MRI-derived Radiomics and Classification of Malignant Brain Tumors.

Malignant brain tumors including parenchymal metastatic (MET) lesions, glioblastomas (GBM), and lymphomas (LYM) account for 29.7% of brain cancers. However, the characterization of these tumors from MRI imaging is difficult due to the similarity of their radiologically observed image features.

Utilizing Artificial Intelligence-Based Deformable Registration for Global and Layer-Specific Cardiac MRI Strain Analysis in Healthy Children and Young Adults.

Rationale And Objectives: The absence of published reference values for multilayer-specific strain measurement using cardiac magnetic resonance (CMR) in young healthy individuals limits its use. This study aimed to establish normal global and layer-specific strain values in healthy children and young adults using a deformable registration algorithm (DRA).

Materials And Methods: A retrospective study included 131 healthy children and young adults (62 males and 69 females) with a mean age of 16.

Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation.

Rationale And Objectives: Imaging-based differentiation between glioblastoma (GB) and brain metastases (BM) remains challenging. Our aim was to evaluate the performance of 3D-convolutional neural networks (CNN) to address this binary classification problem.

Materials And Methods: T1-CE, T2WI, and FLAIR 3D-segmented masks of 307 patients (157 GB and 150 BM) were generated post resampling, co-registration normalization and semi-automated 3D-segmentation and used for internal model development.

Clinical and Magnetic Resonance Imaging Radiomics-Based Survival Prediction in Glioblastoma Using Multiparametric Magnetic Resonance Imaging.

Introduction: Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging-derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS).

Methodology: A retrospective, institutional review board-approved study was performed.

"From Vision to Reality: Virtual Reality's Impact on Baffle Planning in Congenital Heart Disease".

This study aims to evaluate the feasibility and utility of virtual reality (VR) for baffle planning in congenital heart disease (CHD), specifically by creating patient-specific 3D heart models and assessing a user-friendly VR interface. Patient-specific 3D heart models were created using high-resolution imaging data and a VR interface was developed for baffle planning. The process of model creation and the VR interface were assessed for their feasibility, usability, and clinical relevance.

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.

Purpose: To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL).

Methodology: Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR).

Proposed competencies for the performance of cardiovascular computed tomography in pediatric and adult congenital heart disease.

Cardiovascular computed tomography (CCT) is rated appropriate by published guidelines for the initial evaluation and follow up of congenital heart disease (CHD) and is an essential modality in cardiac imaging programs for patients of all ages. However, no recommended core competencies exist to guide CCT in CHD imaging training pathways, curricula development, or establishment of a more formal educational platform. To fill this gap, a group of experienced congenital cardiac imagers, intentionally inclusive of adult and pediatric cardiologists and radiologists, was formed to propose core competencies fundamental to the expert-level performance of CCT in pediatric acquired and congenital heart disease and adult CHD.

Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.

Rationale And Objectives: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex.

Materials And Methods: In this study, we integrated an automated slice range classification step to identify basal to apical short-axis slices before DL-based segmentation.

Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning U-NET Model.

Dynamic magnetic resonance imaging has emerged as a powerful modality for investigating upper-airway function during speech production. Analyzing the changes in the vocal tract airspace, including the position of soft-tissue articulators (e.g.

Utility of cardiac magnetic resonance imaging in diagnosing eosinophilic myocarditis in a patient recently recovered from COVID-19: a grand round case report.

Background: Eosinophilic myocarditis (EM) secondary to eosinophilic granulomatosis with polyangiitis (EGPA) is a rare disease, for which cardiac magnetic resonance imaging (CMRI) is a useful non-invasive modality for diagnosis. We present a case of EM in a patient who recently recovered from COVID-19 and discuss the role of CMRI and endomyocardial biopsy (EMB) to differentiate between COVID-19-associated myocarditis and EM.

Case Summary: A 20-year-old Hispanic male with a history of sinusitis and asthma, and who recently recovered from COVID-19, presented to the emergency room with pleuritic chest pain, dyspnoea on exertion, and cough.

Joint Cardiac Mapping and Cardiac Cine Using Manifold Modeling.

The main focus of this work is to introduce a single free-breathing and ungated imaging protocol to jointly estimate cardiac function and myocardial T1 maps. We reconstruct a time series of images corresponding to k-space data from a free-breathing and ungated inversion recovery gradient echo sequence using a manifold algorithm. We model each image in the time series as a non-linear function of three variables: cardiac and respiratory phases and inversion time.

Imaging of the Rare Association of Truncus Arteriosus in a Neonate with the Ductal Origin of the Left Subclavian Artery Using Multidetector CT Angiography and 3D Rendering.

We present the unreported case of a rare association of truncus arteriosus with the ductal origin of the left subclavian artery. Understanding and preoperative identification of these aortic variations are essential to guide optimal surgical management. In this study, the role of advanced visualization 3D modeling techniques in imaging these complex anomalies is discussed.

Subclinical cardiac involvement in student athletes after COVID-19 infection - Evaluation using feature tracking cardiac MRI strain analysis.

Objectives: To evaluate subclinical cardiac dysfunction in student athletes after COVID-19 infection using feature tracking cardiac MRI strain analysis.

Methods: Student athletes with history of COVID-19 infection underwent cardiac MRI as part of screening before return to competitive play. Subjects were enrolled if they had no or mild symptoms, normal cardiac MRI findings with no imaging evidence of myocarditis.

Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke.

Objectives: Automated image-level detection of large vessel occlusions (LVO) could expedite patient triage for mechanical thrombectomy. A few studies have previously attempted LVO detection using artificial intelligence (AI) on CT angiography (CTA) images. To our knowledge this is the first study to detect LVO existence and location on raw 4D-CTA/ CT perfusion (CTP) images using neural network (NN) models.

Variational Manifold Learning From Incomplete Data: Application to Multislice Dynamic MRI.

Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution magnetic resonance imaging (MRI). We introduce a novel variational approach to learn a manifold from undersampled data.